Motor Control, 2010, 14, e41-e53
© 2010 Human Kinetics, Inc.
Two Archetypes of Motor Control
Research
Mark L. Latash
This reply to the Commentaries is focused on two archetypes of motor control
research, one based on physics and physiology and the other based on control
theory and ideas of neural computations. The former approach, represented by
the equilibrium-point hypothesis, strives to discover the physical laws and salient
physiological variables that make purposeful coordinated movements possible. The
latter approach, represented by the ideas of internal models and optimal control,
tries to apply methods of control developed for man-made inanimate systems to
the human body. Specific issues related to control with subthreshold membrane
depolarization, motor redundancy, and the idea of synergies are briefly discussed.
The nine commentaries represent a broad spectrum of attitudes towards the central ideas of the target article, from highly positive and encouraging to strongly
critical. I would like to start on a positive note and emphasize the clear shift in
the general attitude towards the equilibrium-point (EP) hypothesis. It was not
surprising to read the overall positive commentaries by Feldman, Levin, and
Scholz. However, even the commentators who are known for their critical attitude
to the EP-hypothesis (Shadmehr and Loeb) do not claim that the EP-hypothesis is
meaningless and has been proven to be false. This is definitely a welcome trend,
particularly when compared to the relatively recent fashion of “disproving” the
EP-hypothesis and claiming that it is hopelessly wrong (Gomi & Kawato 1996;
Gottlieb 1998; Hinder & Milner 2003; Popescu et al. 2003). Only five years ago,
one of the commentators wrote: “…convincing evidence shows that the CNS
does not use the equilibrium-point control method…” (Shadmehr & Wise, 2005;
p. 143). The current commentary by Shadmehr avoids such strong statements.
The commentators raise many important issues, and, given the format of this paper,
I will be unable to address them all in sufficient detail. So, I will focus on a few
issues that seem to me central for the current state and the vector of development
of the field of motor control.
Latash is with the Department of Kinesiology, The Pennsylvania State University, University Park, PA.
e41
e42 Latash
Physics of Living Systems vs. Control Theory
Let me start with an axiomatic statement that the main goal of motor control
research is to understand (to come up with a formal description operating with
exactly defined variables) how the central nervous system (CNS) interacts with
the rest of the body and the environment to produce coordinated, purposeful
movements. The body (including the CNS) is composed of physical elements that
may be expected to behave according to the laws of physics. However, physics of
the inanimate nature, while being a highly developed science, has troubles dealing with typical problems of motor control. First, in contrast to movements in the
inanimate world, movements of biological objects are intentional and purposeful. These two notions cannot be easily incorporated into contemporary physics.
Another problem is that the body is a very complex system, maybe too complex to
be studied with the currently available physical tools, and many crucial variables
are not directly measurable or even identifiable. Sometimes, I call motor control
“physics of unobservables” (cf. “partially-observable Markov decision processes”
in the commentary by Gielen). These major problems turn motor control into a
very exciting field of research that requires clever experimental designs and nontrivial theoretical constructs with the ultimate goal of turning this area into physics
of living systems.
In the commentary by Houk, everything downstream from the stretch reflex
is labeled as “physics”. I agree but would like to suggest that everything upstream
(labeled by Houk as behavioral and neuroscience perspectives) is also physics, but
we know much less about its laws and salient variables.
When researchers perform specific studies, they commonly separate the object
of their interest from the rest of the body (and the environment) and assume that this
object receives an input from the rest of the body that may be assigned intelligence.
For example, the notions of motor programs and control variables are a poor-man
way of describing a system, whose physics is unknown. These terms are nothing
more but temporary substitutes reflecting our current inadequate knowledge. All
researchers who accept the physical approach sin in this way, but they hope that,
one day, it will be possible to describe human behavior with a set of physical laws
without resorting to such notions (Gielen seems to agree with this point mentioning
“if any!” with respect to control parameters).
The control theory approach is based on a different set of axioms. It views
the central nervous system as a constellation of computational devices that receive
information and compute control signals leading to actions executed by the rest of
the body (including other computational devices). The body is equipped with sensors, and the CNS performs operations typical of many man-made control systems
such as combining feed-forward and feedback control, optimal control, adaptive
control etc. Within this description, physics stops at the peripheral level (as in
Houk’s commentary, or even earlier), and the CNS has to deal with the complexity
of the physics by performing computations. Researchers who use this approach
typically do not care what physical processes underlie the presumed computations.
Figure 1 illustrates how the two approaches would address a well-known
physical law, that is, the Coulomb law. Figure 1A illustrates the traditional physical
view. A charged particle (an electron) creates an electric field. Any other charged
particle experiences a force with the magnitude proportional to its charge and to
Archetypes of Motor Control Research e43
Figure 1 — A: The physical account of the Coulomb law. An electron creates an electric
field. Another electron experiences a force with the magnitude proportional to the charge
and to the strength of the field. B: A control-theoretical (computational) account. An electron
gets information on the charge and distance from the other electron, computes the force to be
applied to the other electron (FPLAN) and sends a corresponding signal to an actuator, which
transforms the symbolic FPLAN into physical force acting on the other electron.
the strength of the field. Figure 1B illustrates a control theory account. A particle
gets information on the distance that separates it from another particle and on the
charge of the other particle, it performs a relatively simple computational operation
to model the force to be applied to the particle (FPLAN) and sends a corresponding
signal to an actuator, which transforms the symbolic FPLAN into actual force acting
on the particle.
Mathematically, both illustrations lead to the same result. However, I hope that
all the readers would agree that the first one makes sense as a scientific account
of the phenomenon, while the second one, at best, is good for building a model
that can be used to simulate the Coulomb law for high-school students. The first
illustration reflects the physics of the system, while the second one ignores it and
substitutes the physics with highly questionable operations such as electrons measuring distances to other electrons and their charges, performing computations,
and transforming symbolic variables into actual forces.
Certainly, neurons are not electrons, they are much more complex structures.
However, the adequate language for neurons is that of synapses and membrane
potentials, not of forces and mathematical operations. Assuming that neurons
perform computations with mechanical variables and then somehow facilitate
transformation of the symbolic results of these computations into actions represent
major fallacies of the computational approach.
In his commentary, Shadmehr mentions that the optimal feedback scheme
offered by Todorov (2004; also see Todorov & Jordan 2002) is mathematically
e44 Latash
equivalent to the EP-hypothesis (and later – to the uncontrolled manifold hypothesis). Even if this were true (which it is not), the EP-hypothesis tries to understand
and represent the physics of the system (as in Figure 1A), while the optimal feedback
control schemes could not care less about the physics, but offer methods to imitate
the system’s functioning (as in Figure 1B). So, the following statement by Shadmehr
seems to me misleading: “…feedback control is at the heart of both equilibrium
point and optimal feedback control theory…”. Optimal feedback control theory
uses feedback as a means of delivering information for the computational process,
while within the EP-hypothesis, feedback is part of the physics of the system. Along
similar lines, the question “how and where the computations are done?” posed by
Loeb is inapplicable to the EP-hypothesis because no computations are done: It is
physics at work (as in Figure 1A).
The last few statements do not mean that the notion of optimality is inapplicable to biological movement. The commentary by Feldman presents an example
how a criterion of optimality can be incorporated into a theory based on physics
and physiology without assuming neural computations.
To achieve a scientific, physical understanding of the system for movement
production, researchers have to use adequate methods. Using methods developed
for systems that have very little in common with the human body (control theory
was developed for ballistic missiles and other human-built objects) may lead to
important progress in building artificial systems that imitate aspects of human
behavior, with substantial impact in such areas as prosthetics and robotics. But
this is not motor control as defined in the opening paragraph of this section. Being
artificially separated from the physics of the body (including the physics of the
CNS), the control theory approach will never be able to discover how the actual
system for biological movement production works.
Several years ago, Feldman and Latash (2005) stated that the mass-spring
models had played their positive role in the past but had to be abandoned because
they had turned from a stimulating factor into an obstacle for progress of motor
control. Now the time has come to admit that the language of control theory is
inadequate for studying biological objects. It played its role in the past bringing
attention to certain important features of human movement, but the time has come
to leave this approach behind and move forward towards an adequate description
of the biological movement based on physics and physiology.
Shapiro and Körding try to reconcile the different approaches and suggest that
the EP-hypothesis, optimal control, and the idea of internal models can be merged
into a single coherent scheme. Given the very basic differences in the goals and
axioms of the two archetypes of motor control research, I am skeptical. However,
miracles happen. Two hundred years ago, it would be crazy to suggest that a wave
and a particle could represent two characteristics of a single object. That miracle
did happen, albeit within a single field, physics. Merging two clearly separate
fields, physics and control theory, in motor control research may be not easier than
breeding a mammal with a fish.
These two archetypes of motor control research have developed in parallel
for about half a century. Earlier arguments in favor of the physical approach were
beautifully summarized in a classical book by Kugler and Turvey (1987). In the
current commentary Silva, Fonseca, and Turvey continue to support and develop
this approach : “the controller creates energy gradients that direct the movement
Archetypes of Motor Control Research e45
system toward the desired goal state”. Note that this phrase contains “the controller”, that is an intelligent input into the system but, as mentioned earlier, at our
stage of understanding, we have to use such notions as substitutes for the currently
unknown physical processes that play the role of “the controller”. I agree with
Silva, Fonseca, and Turvey that it is crucial to determine what kind of signals
(constraints) modulate control variables according to task demands. To proceed
along this route, we first have to agree what the physical/physiological nature of
the control variables is. The EP-hypothesis offers a hypothetical answer: These are
subthreshold depolarization levels of neuronal pools that result in setting referent
values for salient performance variables. I am unaware of another theory that would
offer a comparably specific answer compatible with neurophysiology.
A Few Issues Related to the Equilibrium-Point
Hypothesis
The two most critical commentaries with respect to the EP-hypothesis make statements regarding inability of the EP-hypothesis to deal with certain facts such as
motor noise (Loeb) and movement patterns in humans with lost proprioception
(Shadmehr). Both issues were addressed in earlier publications (Latash & Gottlieb 1990; Feldman & Levin 1995, 2009; Feldman et al. 1998). In addition, Loeb
chastises the EP-hypothesis for being fixed on muscle spindles (it is not, e.g., Feldman and Latash 1982; Feldman 2009) and also makes a more general claim that
“it is a conjecture, not a testable hypothesis.” The last statement had been made
previously, and it deserves an answer.
Any scientific hypothesis contains established elements that have been proven
beyond reasonable doubt. With respect to the EP-hypothesis, all researchers would
probably agree that the following experimental facts do not need further proof. First,
neurons are threshold elements. Second, voluntary muscle activation is associated
with changes in the activation thresholds of corresponding alpha-motoneuronal
pools. Third, muscle activation levels (and all mechanical variables) are consequences of both descending signals to the corresponding spinal segments and the
external force field that affects inputs from peripheral sensory receptors.
At this level, the EP-hypothesis is indeed not disprovable, not more disprovable that the Coulomb law mentioned earlier (which does not make the Coulomb
law non-scientific). However, further development of the EP-hypothesis allows to
ask questions that may not have unambiguous answers at this time. Some of the
commentaries point at such questions. In particular, Gielen questions whether one
parameter (lambda) per muscle is sufficient to describe its functioning. This is a
non-trivial question. Indeed, the notion of muscle compartments has been well
established (Fleckenstein et al. 1992; Serlin & Schieber 1993). For example, the
deep finger flexor (flexor digitorum profundis) has four distal tendons that attach to
the distal phalanges of the four fingers. Although fingertip forces are not perfectly
independent (Kilbreath & Gandevia 1994; Zatsiorsky et al. 2000), there is a lot of
flexibility in involving individual fingers in multi-finger tasks (reviewed in Latash
et al. 2002, 2007) suggesting that the control of the deep flexor is not limited to one
variable but, more likely, involves at least four lambdas for the four major compartments. This may be true for other muscles with less obvious compartmentalization.
e46 Latash
So, Gielen’s point is well taken: The original description, “one lambda per muscle”,
is a simplification that may have to be modified. This may require, in particular,
accurate experimental studies of the distributions of the stretch reflex inputs across
motor unit subpopulations. Changes in relative involvement of different compartments may happen across tasks performed in different external loading conditions,
for example, isometric and isotonic, as pointed out by Gielen.
Loeb points at potential problems with moving from a joint-level description to
more functional variables. Indeed, recent developments of the EP-hypothesis in the
form of the referent configuration hypothesis try to make this important step (Feldman et al. 2007; Pilon et al. 2007). The importance of analyzing control processes
that focus on functional (task) variables, not being muscle-, joint-, or limb-specific,
is also emphasized by Silva, Fonseca, and Turvey. This is a less developed issue
that offers a lot of exciting questions that are still without answers, that is, it leads to
testable, non-trivial hypotheses, which are most definitely disprovable (for a recent
study within this framework see Latash et al. 2010). The issue of linking processes
at different levels of analysis that ultimately lead to purposeful movements brings
us to the next main topic.
Hierarchical Schemes in Motor Control
In 1966, Bernstein and his two younger colleagues (Bassin et al. 1966; translation published in Lev Latash et al. 1999, 2000) revisited the problem of function
localization within the CNS and suggested that the brain contained localized
operators that could be shared among different functions (cf. Houk’s notion of
distributed processing modules). Figure 1 in the target article explicitly suggests
a hierarchy of operators that produce mappings of a relatively low-dimensional
input (that defines the task for this particular level) to a higher-dimensional output
organized to stabilize the combined action of that level at a value compatible with
the input. Two commentators, Houk and Loeb, disagree and state that, according
to neurophysiological data, dimensionality is decreasing, not increasing, from
hierarchically higher levels to hierarchically lower ones. I agree with Loeb that
we do not know how the goals of the task are represented in the CNS. So, we do
not know whether the high-dimensional neurophysiological processes mentioned
by Houk and Loeb are related to task representation or to results of processing of
such a representation with several of the assumed few-to-many transformations.
It seems natural to assume that representation of a simple, laboratory task in a
reproducible environment is as low-dimensional as the task itself, as formulated
by the experimenter and understood by the subject.
In a recent paper (Latash 2010), a generic physiological mechanism for an
operator that may perform the presumed few-to-many mappings has been suggested.
Figure 2 illustrates such a generic scheme using control with threshold values for
neuronal activation. The controller (to remind, this word is used for a physical
system whose structure and functioning are currently unknown, not for a computational system) sends a signal to a neuronal pool (N); this signal depolarizes the
target cell membranes and modulates the threshold for action potential generation
by N. Output of N (efferent command, EFF) is distributed along a branching tree
of similar operators (not shown in the Figure) such that each output of the “top”
Archetypes of Motor Control Research e47
Figure 2 — An illustration of control with referent configurations. A: A command expressed
in membrane potential units (V) is sent to a neuronal pool (N); this signal depolarizes
the target cell membranes and modulates the threshold for action potential generation by
N. Output of N (efferent command, EFF) is distributed along a branching tree of similar
operators (not shown in the Figure) such that each output of the “top” operator serves as
an input into a hierarchically lower operator. Ultimately, the process results in an input
into alpha-motoneuronal pools and, via the tonic stretch reflex, leads to changes in muscle
activations and in the actual configuration of the body (AC). Given external conditions, the
signal from the controller specifies a referent configuration (RC), defined as a configuration, at which all the muscles would be at thresholds for their activation. A sensor neuron
provides feedback (afferent signal, AFF) to neurons N reflecting the difference between
RC and AC. B: The activation of the neurons produces a mechanical effect that moves AC
towards RC. When the two coincide, N becomes silent, and the system stays at RC. If an
external load (L) prevents AC from moving, an equilibrium state is reached corresponding
to non-zero neuronal (and muscular) activation and a combination of positional and force
variables. Modified with permission from Latash 2010.
operator serves as an input into a hierarchically lower operator. This scheme is
compatible with Feldman’s notion of a hierarchy of frames of reference. To borrow
another metaphor from Feldman, setting the hierarchically highest referent frame
may be compared to carrying a set of objects in a box. The hierarchy suggests that
there may be boxes within the larger boxes and, depending on the task, they may
themselves be moved within the largest box, for example when the task is not only
to move the fingertip to a target but also to produce a certain movement by one of
the involved joints.
Ultimately, the process results in an input into alpha-motoneuronal pools
and, via the tonic stretch reflex, leads to changes in muscle activations and in the
actual configuration of the body (AC). Given external conditions, the signal from
the controller may also be viewed as specifying a referent configuration (RC),
defined as a configuration, at which all the muscles would be at thresholds for their
activation. A sensor neuron provides feedback (afferent signal, AFF) to neurons N
reflecting the difference between RC and AC (Fig. 2A). For data compatible with
this scheme see Pilon et al. 2007; Latash et al. 2010.
e48 Latash
If AC differs from RC (to the right of RC in Fig. 2B), the neurons N are
activated and this activation is higher for larger deviations of AC from RC. The
activation of the neurons produces a mechanical effect that moves AC towards RC.
When the two coincide, N become silent, and the system stays at RC. If something
(for example, an external load, L) prevents AC from moving, an equilibrium state
is reached corresponding to non-zero neuronal (and muscular) activation and a
combination of positional and force variables. In the target article, I mentioned the
principle of minimal final action guiding the process. Gielen criticizes this principle referring to studies that show non-zero muscle coactivation in the final state.
I failed to emphasize in the target article that the notion of referent configuration
involves the coactivation command (c-command, Feldman 1980). Hence, the final
action is expected to be minimal given the referent configuration (including the
c-command) and the external forces.
Figure 2 illustrates how control with thresholds of activation of neuronal pools
may drive the body to its referent configuration. Where do all these processes
happen? At this time, we do not know. So, I cannot answer Loeb’s question where
to stick an electrode to measure all those variables. It is even possible that such a
place does not exist, for example neurons N may be distributed over a vast area
within the CNS. However, not having a place to stick an electrode is not atypical
for physics (in what part of an elementary particle would you stick an electrode to
measure its electric field?), and it does not make physics less scientific.
What Is a Synergy?
The term synergy” has been used in the field of movement studies for over a century.
As mentioned by Levin, in clinical studies, this word has a negative connotation
implying stereotypical muscle activation patterns that differ from those observed
in healthy individuals. This term had to be defined explicitly and operationally to
make it useful for motor control studies. The definition offered in the target article
(and in earlier papers, reviewed in Latash et al. 2007, Latash 2008) is based on
several premises including the premise of motor redundancy. Although Loeb states
that this premise may be false, the well documented phenomena of motor variability in laboratory tasks and the ability of humans to perform virtually any task
in a variety of ways (cf. “repetition without repetition”, Bernstein 1947) suggest
that motor redundancy does exist. Feldman also comments on the issue of redundancy and suggests that the size principle leading to the rank-ordered recruitment
of motor units can turn the problem of sharing activation among motor units into
a non-redundant one. This is a good point. However, since each motor unit can be
recruited at various frequencies, the redundancy is not eliminated by the size principle but only reduced. The size principle is a good illustration of a more general
principle summarized by Feldman: “The brain narrows the amount of redundancy
and allows natural interactions of the system to bring about unique actions”.
Much effort has been spent recently on defining synergy in a way that allows
to identify a synergy from a non-synergy and to quantify synergies (reviewed in
Latash et al. 2007; Latash 2008). I believe that this effort was well spent. At a recent
panel discussion during the Progress in Motor Control meeting in Marseille, one
of my respected colleagues stated that researchers in motor control should stop
Archetypes of Motor Control Research e49
wasting time arguing about terms (the discussion was about internal models, see
the commentary by Shapiro and Körding). I could not disagree more. Arguing
about the meaning of terms is not a waste of time, using terms without a clear
understanding of their meaning is.
So, I view the definition of synergy as an important step in making this notion
useful not only for clinicians but also for motor control researchers. As noticed by
Feldman, the offered definition uses the notion of action repetition. As of now, there
has been only one study that used this definition and the associated computational
apparatus to study synergies within a single trial (Scholz et al. 2003). This approach
is particularly important to study synergies in atypical persons who may not be
able to repeat the same action many times. One problem of such analysis is that
multi-element actions are usually associated with time changes of the Jacobian of
the system. This introduces complications into quantitative estimation of the two
components of variance (“good” and “bad”). These complications are amplified by
non-independence of observations within a time series. Nevertheless, the example
of the cited study shows that these complicating factors can be overcome, at least
for some actions.
The notion of synergy is tightly linked to the issue of stability of important
performance variables (Scholz), a feature that is ensured by flexible involvement
of elementary variables (Scholz & Schöner 1999). In his commentary, Scholz
makes an important point that reducing the two components of variance, “good”
and “bad”, to a single index (ΔV) may be too crude. Indeed, similar changes in ΔV
may be induced by changes in VGOOD, VBAD, or both. For example, an increase in
ΔV may reflect more accurate performance with no other changes (smaller VBAD,
VGOOD = const) or more flexible patterns of elementary variables without a change
in accuracy of the important performance variable (larger VGOOD, VBAD = const).
Within the language of the EP-hypothesis, the former scenario implies using more
reproducible referent configurations across trials, while the latter one implies
more variable transformations of any given referent configuration into patterns of
elementary variables (see Figure 1 in the target article).
Challenges and Directions of Further Studies
The commentary by Silva, Fonseca, and Turvey suggests that the notion of synergy
should be expanded to cover sensory processes. Indeed, afferent redundancy may
be not as obvious as the motor one, but it is likely to be as common during most
actions. In a rather old paper, Feldman and Latash (1982) discussed how position
and force sense might be interpreted in the framework of the EP-hypothesis (for a
recent update on this topic see Feldman 2009). They suggested that position and
force sense for a muscle emerged as a point of intersection between the muscle
invariant characteristic (tonic stretch reflex characteristic) and an “afferent curve”
that was assumed to represent a weighted sum of afferent signals from all the
proprioceptors. This mechanism allows, in particular, to make position/force sense
immune to malfunctioning of any one of the sources of sensory information. The
idea of a “tensegrity system” described by Silva, Fonseca, and Turvey seems to be
close in spirit to the described more simplistic idea of a weighted sum of sensory
signals. The issue of sensory synergies has also been discussed in one of the recent
e50 Latash
chapters (Latash 2008). The purpose of a sensory synergy was defined as creating a
stable percept, in particular of the body. This idea seems close to using all afferents
together in creating a coherent image of the body (in the environment).
As of now, there have been only a few attempts to link the notion of synergies,
as defined in the target article, to impaired movements (Latash et al. 2003; Reisman
& Scholz 2003, 2006; Olafsdottir et al. 2007). Those studies used the uncontrolled
manifold toolbox to quantify synergies in a variety of motor tasks and emphasized
differences in the performance of such populations as persons with Down syndrome,
stroke survivors, and the healthy elderly. They did not discuss possible relations of
the findings to impairments in producing patterns of control variables within the
EP-hypothesis. Levin makes an excellent point emphasizing possible links between
abnormal synergies and the demonstrated limitations in the range of shifts of the
control variables (lambdas) in spasticity (Levin et al. 2000; Musampa et al. 2007). It
may not be trivial to merge these ideas with those of hierarchical control with referent configurations, but the formulation of the problem is challenging and enticing.
In the target article, I mentioned lack of tools to measure lambda as a major challenge for development of the field. Gielen disagrees and states that such tools exist.
Further he refers to tools for accurate measurement of levels of muscle activation,
which are reflective of both lambda and sensory feedback. These tools may indeed
be useful for analysis of thresholds of the tonic stretch reflex; however, applying
these tools to extract time profiles of lambdas during movements may not be trivial.
Two commentaries, by Loeb and by Shapiro and Körding, emphasize that it
is necessary to incorporate motor and sensory noise into any motor control theory.
I agree, although it is not always clear what is meant under these terms. There is
no argument that signals (sequences of action potentials) produced by elements of
the sensory-motor system (individual receptors and motor units) are noisy. However, accuracy in some of the sensory-motor tasks is stunning. In his classic “On
Dexterity and Its Development”, Bernstein (1996) estimated errors in the direction
of force vector applied to a billiard ball during a successful shot as being about
one angular minute. Errors of professional archers, marksmen, and pool players
are probably even lower. How is this amazingly accurate performance built on
noisy elements? One purpose of the notions of motor synergies and sensory synergies is indeed to allow actions to be relatively immune to noise of the elements.
A few studies (Shinohara et al. 2003, 2004) have shown that multi-effector tasks
can overcome the seemingly unavoidable increase in dispersion with magnitude
of variables such as force (Newell & Carlton 1993; Harris & Wolpert 1998). In
such tasks, each element shows the expected increase in force standard deviation
with force magnitude, but the co-variation among forces of the elements allows to
keep standard deviation of total force low and nearly constant over a wide range
of force magnitudes.
Shapiro and Körding focus on the ability of animals to behave in a predictive fashion. A recent paper (Stepp & Turvey 2010) contrasts predictions using
computational means (weak anticipation) and predictions built on physics (strong
anticipation). It emphasized the insurmountable problems posed by the enormous
redundancy at the level of synaptic inputs into neurons for any kind of neural prediction of mechanical variables (see Foisy & Feldman 2006). It would indeed be great
to know the physics and physiology of predictive processes within the body, but at
our level of knowledge this remains wishful thinking. For now, we have to resort to
Archetypes of Motor Control Research e51
imprecise terms such as “neural representation” to reflect the ability of biological
systems to predict and to behave in a probabilistic fashion (see the highly relevant
and commonly ignored studies on “probabilistic prognosis” by Feigenberg 1969,
1998). It seems much more honest and productive to openly admit that we do not
know the physics of those processes than to substitute them with computational
operations assigned to brain structures.
I would like to end on a positive note by thanking the commentators for their
open and honest opinions that are so rarely expressed and so badly needed in the
field of motor control. I hope that “Motor Control” will continue publishing such
mini-discussions on hot topics that expose different views and, hopefully, serve as
important sources for both seasoned and beginning researchers.
Acknowledgments
I am grateful to Anatol Feldman, Simon Goodman, and Vladimir Zatsiorsky for the helpful comments and suggestions. The preparation of this paper was partly supported by NIH
grants AG-018751 and NS-035032.
References
Bassin, P.V., Bernstein, N.A. & Latash, L.P. (1966) On the problem of the relation between
structure and function in the brain from a contemporary viewpoint. In: Grastschenkov,
N.I. (Ed.) Physiology in clinical practice, pp. 38-71, Nauka: Moscow.
Bernstein, N.A. (1947) On the Construction of Movements. Medgiz: Moscow (in Russian).
Bernstein, N.A. (1996) On dexterity and its development. In: In: Latash, M.L. & Turvey,
M.T. (Eds.) Dexterity and Its Development, pp. 1-244, Erlbaum Publ.: Mahwah, NJ
Feigenberg, I.M. (1969) Probabilistic prognosis and its significance in normal and pathological subjects. In: Cole M, Maltzman I (Eds) Handbook of Contemporary Soviet
Psychology, p. 355-369, Basic Books: London, New York.
Feigenberg, I.M. (1998) The model of the future in motor control. In: Latash, M.L. (Ed.)
Progress in Motor Control, vol. 1, Bernstein’s Traditions in Movement Studies, p.
89-104, Human Kinetics: Urbana, IL.
Feldman, A.G. (1980) Superposition of motor programs. I. Rhythmic forearm movements
in man. Neuroscience 5: 81-90
Feldman, A.G. (2009) New insights into action-perception coupling. Experimental Brain
Research 194: 39-58.
Feldman, A.G., Goussev, V., Sangole, A. & Levin, M.F. (2007) Threshold position control
and the principle of minimal interaction in motor actions. Progress in Brain Research
165: 267-281.
Feldman, A.G. & Latash, M.L. (1982) Interaction of afferent and efferent signals underlying
joint position sense: Empirical and theoretical approaches. Journal of Motor Behavior
14: 174-193.
Feldman, A.G. & Latash, M.L. (2005) Testing hypotheses and the advancement of science: Recent attempts to falsify the equilibrium-point hypothesis. Experimental Brain
Research 161: 91-103.
Feldman, A.G. & Levin, M.F. (1995) Positional frames of reference in motor control: their
origin and use. Behavioral and Brain Sciences 18: 723-806.
Feldman, A.G. & Levin, M.F. (2009) The equilibrium-point hypothesis--past, present and
future. Adv Exp Med Biol 629, 699-726.
e52 Latash
Feldman, A.G., Ostry, D.J., Levin, M.F., Gribble, P.L. & Mitnitski, A.B. (1998) Recent tests
of the equilibrium-point hypothesis (λ model). Motor Control 2: 189-205
Fleckenstein, J.L., Watumull, D., Bertocci, L.A., Parkey, R.W. & Peshock, R.M. (1992)
Finger-specific flexor recruitment in humans: depiction by exercise-enhanced MRI.
Journal of Applied Physiology 72: 1974-1977.
Foisy, M. & Feldman, A. (2006) Threshold control of arm posture and movement adaptation
to load. Experimental Brain Research 175: 726–744.
Gomi, H. & Kawato, M. (1996) Equilibrium-point hypothesis examined by measured arm
stiffness during multijoint movement. Science 272: 117-120.
Gottlieb, G.L. (1998) Rejecting the equilibrium-point hypothesis. Motor Control 2: 10-12.
Harris, C.M. & Wolpert, D.M. (1998). Signal-dependent noise determines motor planning.
Nature 394: 780-784.
Hinder, M.R. & Milner, T.E. (2003) The case for an internal dynamics model versus equilibrium point control in human movement. Journal of Physiology 549: 953-963.
Kilbreath, S.L. & Gandevia, S.C. (1994) Limited independent flexion of the thumb and
fingers in human subjects. Journal of Physiology 479: 487-497
Kugler, P.N. & Turvey, M.T. (1987) Information, natural law, and the self-assembly of
rhythmic movement. Hillsdale, NJ: Erlbaum.
Latash, L.P., Latash, M.L. & Mejier, O.G. (1999) Thirty years later: On the problem of the
relation between structure and function in the brain from a contemporary viewpoint
(1966). Part I. Motor Control 3: 329-345.
Latash, L.P., Latash, M.L. & Mejier, O.G. (2000) Thirty years later: On the problem of the
relation between structure and function in the brain from a contemporary viewpoint
(1966). Part II. Motor Control 4: 125-149.
Latash, M.L. (2008) Synergy. Oxford University Press: New York.
Latash, M.L. (2010) Stages in learning motor synergies: A view based on the equilibriumpoint hypothesis. Human Movement Science (in press).
Latash, M.L., Friedman, J., Kim, S.W., Feldman, A.G. & Zatsiorsky, V.M. (2010) Prehension synergies and control with referent hand configurations. Experimental Brain
Research (in press).
Latash, M.L. & Gottlieb, G.L. (1990) Equilibrium-point hypothesis and variability of the
amplitude, speed, and time of single-joint movements. Biofizika 35: 870-874
Latash, M.L., Kang, N. & Patterson, D. (2002) Finger coordination in persons with Down
syndrome: Atypical patterns of coordination and the effects of practice. Experimental
Brain Research 146: 345-355.
Latash, M.L., Scholz, J.P. & Schöner, G. (2002) Motor control strategies revealed in the
structure of motor variability. Exercise and Sport Science Reviews 30: 26-31.
Latash, M.L., Scholz, J.P. & Schöner, G. (2007) Toward a new theory of motor synergies.
Motor Control 11: 275-307.
Levin, M.F., Selles, R.W., Verheul, M.H.G. & Meijer, O.G. (2000) Deficits in the coordination of agonist and antagonist muscles in stroke patients: Implications for normal motor
control. Brain Research 853: 352-369.
Musampa, N.K., Mathieu, P.A. & Levin, M.F. (2007) Relationship between stretch reflex
thresholds and voluntary arm muscle activation in patients with spasticity. Experimental
Brain Research 181: 579-593.
Newell, K.M. & Carlton, L.G. (1993) Force variability in isometric responses. Journal of
Experimental Psychology: Human Perception and Performance 14: 37-44.
Olafsdottir, H., Zhang, W., Zatsiorsky, V.M. & Latash, M.L. (2007) Age related changes in
multi-finger synergies in accurate moment of force production tasks. Journal of Applied
Physiology 102: 1490-1501.
Pilon, J.-F., De Serres, S.J. & Feldman, A.G. (2007) Threshold position control of arm movement with anticipatory increase in grip force. Experimental Brain Research 181: 49-67
Archetypes of Motor Control Research e53
Popescu, F.C., Hidler, J.M. & Rymer, W.Z. (2003) Elbow impedance during goal-directed
movements. Experimental Brain Research 152: 17-28
Reisman, D.S. & Scholz, J.P. (2003). Aspects of joint coordination are preserved during
pointing in persons with post-stroke hemiparesis. Brain 126:2510-2527.
Reisman, D.S. & Scholz, J.P. (2006) Workspace location influences joint coordination
during reaching in post-stroke hemiparesis. Experimental Brain Research 170: 265-276.
Scholz, J.P., Kang, N., Patterson, D. & Latash, M.L. (2003) Uncontrolled manifold analysis
of single trials during multi-finger force production by persons with and without Down
syndrome. Experimental Brain Research 153: 45-58.
Scholz, J.P. & Schöner, G. (1999). The uncontrolled manifold concept: Identifying control
variables for a functional task. Experimental Brain Research 126, 289-306.
Serlin, D.M. & Schieber, M.H. (1993) Morphologic regions of the multitendoned extrinsic
finger muscles in the monkey forearm. Acta Anatomica 146: 255-266.
Shadmehr, R. & Wise, S.P. (2005) The computational neurobiology of reaching and pointing. MIT Press: Cambridge, MA.
Shinohara, M., Li, S., Kang, N., Zatsiorsky, V.M. & Latash, M.L. (2003) Effects of age and
gender on finger coordination in maximal contractions and submaximal force matching
tasks. Joural of Applied Physiology 94: 259-270.
Shinohara, M., Scholz, J.P., Zatsiorsky, V.M. & Latash, M.L. (2004) Finger interaction during
accurate multi-finger force production tasks in young and elderly persons. Experimental
Brain Research 156: 282-292.
Stepp, N. & Turvey, M.T. (2010) On strong anticipation. Cognitive Systems Research 11:
148-164.
Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience
7: 907-915.
Todorov, E. & Jordan, M.I. (2002) Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5: 1226-1235.
Zatsiorsky, V.M., Li, Z.M. & Latash, M.L. (2000) Enslaving effects in multi-finger force
production. Experimental Brain Research 131: 187-195.